1. Field of the Invention
The present invention relates to an image processing apparatus and an image retrieval method that retrieves a similar image in accordance with a degree of similarity of a feature point that is within the image.
2. Description of the Related Art
Conventionally, a retrieval that supports a rotation of an image performs a retrieval by employing either a technique of rotating a query image and deriving a feature amount, or else a technique of making a rotational transformation of the feature amount. A technology also exists that takes into account a distribution for a normalizing a value of each respective feature amount with regard to a learning image, i.e., a sample image wherein the distribution of the value of the feature amount is checked, and a matching is performed that employs a Bayes' distance or a Mahalanobis distance.
Such a technology, however, involves the feature amount distribution being normalized with regard to the learning image as an ideal of a registered image, and does not take into account that the feature amount thus calculated may experience a discrepancy as a result of a image process, such as a rotation, an enlargement, or a reduction, of a query image. As a consequence, a distance from one feature amount to another feature amount for a purpose of computing a degree of similarity is not accurately computed, and a discrepancy arises therefrom.
Japanese Patent Laid Open No. 2006-065399 discloses an image retrieval technology in accordance with the degree of similarity of a feature point within the image. The technology involves making a sorting selection of the feature point and the feature amount that is registered with an image process lexicon using a prepared model image for learning. After the feature point and the feature amount are extracted from the image for learning, the feature point and the feature amount thus extracted is compared with the feature point and the feature amount of the learning model image. A feature amount that has a maximum number of instances wherein it is determined to correspond thereto is thereby registered in the lexicon as a feature amount that is employed in the recognition process thereof.
An extraction of the feature point and the feature amount is performed after performing a multiple resolution or a rotation transformation, and the feature point and the feature amount thus extracted is made into an index with a tree structure that allows a lexicon for recognition to be easily built. A specific description from a viewpoint of robustness as relates to the enlargement or the reduction of the image, a degree of distribution of the feature amount or a rotation of the feature point of the query image itself, however, is lacking.
Conversely, according to C. Schmid and R. Mohr, “Local grayvalue invariants for image retrieval,” IEEE Trans. PAMI., Vol. 19, No. 5, pp. 530-535, 1997 (hereinafter “Cited Reference 1”), a detection of the feature point is performed, the feature amount is calculated that incorporates a property of a plurality of resolutions that will be invariant under the rotation, and a feature point thereof wherein a value is a maximum is adopted as being a robust feature. The comparison with the query image employs the Mahalanobis distance that takes into account the distribution of the feature amount of the registered image, and thereby mitigates the problem of a bias in a multidimensional feature amount distribution.
The feature amount that is defined as incorporating the property of being invariant under the rotation, however, does actually incur a fluctuation thereunder as a result of a noise of such as a linear interpolation that is carried out when rotation processing the image. FIG. 8 depicts an instance of the fluctuation that actually occurs in the feature amount that is defined as incorporating the property of being invariant under the rotation, wherein is depicted the fluctuation thereunder of a zeroth component and a first component of the feature amount when rotated in 15-degree increments from zero to 90 degrees. As can be seen in FIG. 8, the feature amount that is defined as incorporating the property of being invariant under the rotation does, in fact, fluctuate thereunder. A problem arises wherein a precision of the retrieval declines as a consequence of the problem of the feature amount that is supposed to be invariant fluctuating thereunder in actuality.
The problems occurs because the distribution of the multidimensional feature amount of the registered feature amount is only normalized by the Mahalanobis distance, and the discrepancy of the distribution of the feature point of the query image that arises from the enlargement, the reduction, or the rotation thereof is not taken into account. The feature amount that is defined as incorporating the property of being invariant under the rotation while, in fact, fluctuating thereunder, will be treated hereinafter as a local feature amount. A point that includes the local feature amount will be treated hereinafter as a local feature point.
As per the preceding description, the discrepancy that occurs as a result of the rotation, the reduction, or the rotation of the query itself of the query image proper is not taken into account under the conventional technology, and thus, a calculation of the degree of similarity of a image and another image cannot be said to include a high degree of precision.